0e04457539
Phase 1 - Session Bootstrap: - Add .claude/ATOMIZER_CONTEXT.md as single entry point for new sessions - Add study state detection and task routing Phase 2 - Code Deduplication: - Add optimization_engine/base_runner.py (ConfigDrivenRunner) - Add optimization_engine/generic_surrogate.py (ConfigDrivenSurrogate) - Add optimization_engine/study_state.py for study detection - Add optimization_engine/templates/ with registry and templates - Studies now require ~50 lines instead of ~300 Phase 3 - Skill Consolidation: - Add YAML frontmatter metadata to all skills (versioning, dependencies) - Consolidate create-study.md into core/study-creation-core.md - Update 00_BOOTSTRAP.md, 01_CHEATSHEET.md, 02_CONTEXT_LOADER.md Phase 4 - Self-Expanding Knowledge: - Add optimization_engine/auto_doc.py for auto-generating documentation - Generate docs/generated/EXTRACTORS.md (27 extractors documented) - Generate docs/generated/TEMPLATES.md (6 templates) - Generate docs/generated/EXTRACTOR_CHEATSHEET.md Phase 5 - Subagent Implementation: - Add .claude/commands/study-builder.md (create studies) - Add .claude/commands/nx-expert.md (NX Open API) - Add .claude/commands/protocol-auditor.md (config validation) - Add .claude/commands/results-analyzer.md (results analysis) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
43 lines
1.2 KiB
Python
43 lines
1.2 KiB
Python
#!/usr/bin/env python
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"""
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{STUDY_NAME} - Neural Network Acceleration Script (Simplified)
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================================================================
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This script uses ConfigDrivenSurrogate for config-driven NN optimization.
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The ~600 lines of boilerplate code is now handled automatically.
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Workflow:
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---------
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1. First run FEA: python run_optimization.py --run --trials 50
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2. Then run NN: python run_nn_optimization.py --turbo --nn-trials 5000
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Or combine:
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python run_nn_optimization.py --all
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Generated by Atomizer StudyWizard
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"""
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from pathlib import Path
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import sys
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# Add project root to path
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project_root = Path(__file__).resolve().parents[2]
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sys.path.insert(0, str(project_root))
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from optimization_engine.generic_surrogate import ConfigDrivenSurrogate
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def main():
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"""Run neural acceleration using config-driven surrogate."""
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# Create surrogate - all config read from optimization_config.json
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surrogate = ConfigDrivenSurrogate(__file__)
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# Element type: 'auto' detects from DAT file
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# Override if needed: surrogate.element_type = 'cquad4' (shell) or 'ctetra' (solid)
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return surrogate.run()
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if __name__ == "__main__":
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exit(main())
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